HousingPredictionApp

California Housing Price Prediction 🏠

This is a machine learning project where I built a model to predict the median house value of a district using the California Housing dataset.

I wanted to understand the complete workflow of a regression problem instead of just training a model, so the project includes data splitting, preprocessing, model comparison, cross-validation, and finally using the trained model to make predictions.

What I used

• Python
• Pandas
• NumPy
• Scikit-learn
• Joblib

Dataset

The dataset contains information about different housing districts in California, including:

• Median income
• Housing median age
• Average number of rooms
• Average number of bedrooms
• Population
• Average household size
• Ocean proximity
• Latitude and longitude

The target variable is median_house_value.

How the project works

1. Train-Test Split

I used StratifiedShuffleSplit to divide the dataset into training and testing data.

I created an income_cat column based on median_income so that the income distribution remains similar in both sets.

2. Data Preprocessing

For numerical features, I used:

• Median imputation for missing values
• Standard scaling

For the categorical ocean_proximity feature, I used one-hot encoding.

I combined these using Scikit-learn's Pipeline and ColumnTransformer.

3. Models

I tested three different regression models:

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor

I used 10-fold cross-validation and RMSE to compare their performance.

Results

Lower RMSE means better performance, so Random Forest performed the best.

It reduced the RMSE by around 28.8% compared with Linear Regression, which made it the model I chose for the final prediction system.

Model Saving & Prediction

After choosing Random Forest, I saved both the trained model and preprocessing pipeline using Joblib:

joblib.dump(model, "model.pkl")
joblib.dump(pipeline, "pipeline.pkl")

For new data, the saved pipeline preprocesses the input and the saved model generates the predicted house values.

The predictions are saved in output.csv.

Project Structure

California-Housing-Price-Prediction/
│
├── housing.csv
├── input.csv
├── output.csv
├── ModelTest.py
├── main.py
├── model.pkl
├── pipeline.pkl
└── README.md

How to Run

Install the required libraries:

pip install pandas numpy scikit-learn joblib

Run the project:

python main.py

The model will be trained if model.pkl doesn't already exist. Otherwise, the saved model will be used to make predictions from input.csv.

What I learned

Through this project, I got hands-on experience with:

• Regression problems
• Train-test splitting
• Stratified sampling
• Data preprocessing
• Pipelines
• One-hot encoding
• Cross-validation
• RMSE
• Comparing different ML models
• Saving and loading trained models
• Making predictions on new data

Future Improvements

Some things I would like to add later:

• Hyperparameter tuning
• More feature engineering
• Testing more regression models
• Building a simple web interface
• Deploying the model

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Made by Mohit Raj AI & Data Science Student